World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
53
Citations
12212
World Ranking
4792
National Ranking
2229

Overview

Hans Peter Graf is affiliated with NEC in the United States and has contributed to research primarily in the field of Computer Science. Their work encompasses several subfields, notably Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Biophysics, and Signal Processing.

Their research topics include:

  • Human Pose and Action Recognition
  • Multimodal Machine Learning Applications
  • Video Analysis and Summarization
  • Anomaly Detection Techniques and Applications
  • AI in cancer detection
  • Radiomics and Machine Learning in Medical Imaging
  • Cell Image Analysis Techniques

Hans Peter Graf has published papers in various venues, including:

  • arXiv (Cornell University)
  • The Journal of Pathology Clinical Research
  • Journal of Physics Conference Series

Recent papers reflect an emphasis on applications of artificial intelligence and machine learning in both medical and computer vision domains:

  • "Development of multiple AI pipelines that predict neoadjuvant chemotherapy response of breast cancer using H&E-stained tissues" (2023), The Journal of Pathology Clinical Research
  • "S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation" (2020), arXiv (Cornell University)
  • "Hopper: Multi-hop Transformer for Spatiotemporal Reasoning" (2021), arXiv (Cornell University)
  • "COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only Modality" (2021), arXiv (Cornell University)
  • "Learning Higher-order Object Interactions for Keypoint-based Video Understanding" (2023), arXiv (Cornell University)

The researcher collaborates frequently with several co-authors who contribute to topics in related fields. Frequent collaborators include:

  • Asim Kadav
  • Farley Lai
  • Honglu Zhou
  • Mubbasir Kapadia
  • Aviv Shamsian

Overall, Hans Peter Graf's body of work focuses extensively on computer vision and machine learning techniques, particularly their use in medical imaging and human activity recognition contexts.

Best Publications

  • Pruning Filters for Efficient ConvNets

    Hao Li;Asim Kadav;Igor Durdanovic;Hanan Samet

  • Handwritten digit recognition: applications of neural network chips and automatic learning

    Y. Le Cun;L.D. Jackel;B. Boser;J.S. Denker

  • Parallel Support Vector Machines: The Cascade SVM

    Hans P. Graf;Eric Cosatto;Léon Bottou;Igor Dourdanovic

  • Massively parallel processing core with plural chains of processing elements and respective smart memory storing select data received from each chain

    Srihari Cadambi;Abhinandan Majumdar;Michela Becchi;Srimat Chakradhar

  • An image transform approach for HMM based automatic lipreading

    G. Potamianos;H.P. Graf;E. Cosatto

  • A Massively Parallel Coprocessor for Convolutional Neural Networks

    Murugan Sankaradas;Venkata Jakkula;Srihari Cadambi;Srimat Chakradhar

  • Visual prosody: facial movements accompanying speech

    H.P. Graf;E. Cosatto;V. Strom;Fu Jie Huang

  • VLSI implementation of a neural network memory with several hundreds of neurons

    H. P. Graf;L. D. Jackel;R. E. Howard;B. Straughn

  • VLSI implementation of a neural network model

    Hans P. Graf;Lawrence D. Jackel;Wayne E. Hubbard

  • Neural Network Recognizer for Hand-Written Zip Code Digits

    John S. Denker;W. R. Gardner;Hans Peter Graf;Donnie Henderson

  • Face feature analysis for automatic lipreading and character animation

    Hans Peter Graf;Eric David Petajan

  • Multi-modal system for locating heads and faces

    H.P. Graf;E. Cosatto;D. Gibbon;M. Kocheisen

  • System and method of providing conversational visual prosody for talking heads

    Eric Cosatto;Hans Peter Graf;Thomas M. Isaacson;Volker Franz Strom

  • Photo-realistic talking-heads from image samples

    E. Cosatto;H.P. Graf

  • A reconfigurable VLSI neural network

    S. Satyanarayana;Y.P. Tsividis;H.P. Graf

  • Method and apparatus for separating static and dynamic portions of document images

    Hans P. Graf;Daniel J. Mayer

  • Discriminative training of HMM stream exponents for audio-visual speech recognition

    G. Potamianos;H.P. Graf

  • Image skeletonization method

    John S. Denker;Hans P. Graf;Donnie Henderson;Richard E. Howard

  • System and method of controlling sound in a multi-media communication application

    Joern Ostermann;Mehmat Reha Civanlar;Hans Peter Graf;Thomas M. Isaacson

  • Method for locating a subject's lips in a facial image

    Hans Peter Graf

Frequent Co-Authors

Lawrence D. Jackel
Lawrence D. Jackel Toyota Research Institute
Richard Howard
Richard Howard Rutgers, The State University of New Jersey
John S. Denker
John S. Denker Nokia (United States)
Srimat T. Chakradhar
Srimat T. Chakradhar NEC (United States)
Bernhard E. Boser
Bernhard E. Boser University of California, Berkeley
Gerasimos Potamianos
Gerasimos Potamianos University Of Thessaly
Isabelle Guyon
Isabelle Guyon University of Paris-Saclay
Yann LeCun
Yann LeCun Facebook (United States)
Yannis Tsividis
Yannis Tsividis Columbia University
Henry S. Baird
Henry S. Baird Lehigh University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Studying Computer Science in the USA opens doors to a range of dynamic online degree options and promising career pathways. For those looking to quickly enter the workforce, the fastest computer science degree programs let you fast-track your education without compromising on quality. These programs are ideal if you want to complete your studies efficiently and start your tech career sooner.

Beyond computer science, related fields such as environmental science and engineering offer strong career prospects as well. If you’re interested in sustainability and technology, exploring an online environmental engineering degree science and engineering can lead to roles in clean energy, resource management, and sustainable tech innovations.

For those who are cost-conscious, the online mechanical engineering degree is another affordable and flexible pathway into high-demand STEM fields. Similarly, if you have an interest in environmental studies, pursuing careers in high-paying jobs with environmental science degree can be both rewarding and lucrative.

These interconnected disciplines and online study options make it easier than ever to shape your ideal career in science, engineering, and technology—no matter where you start.

Best Scientists Citing Hans Peter Graf

Trending Scientists

Recently Published Articles